# IMG_PATH = "../input/animals-data/dataset/"
# IMG_DATA_PATH = "../data/small_data_oil_for_classification/images/"
# TRAIN_DATA_PATH = "../data/small_data_oil_for_classification/train/"
# TEST_DATA_PATH = "../data/small_data_oil_for_classification/test/"
IMG_DATA_PATH = "../data/small_data_oil_for_classification/images/"
TRAIN_DATA_PATH = "F:\\DataSet\\tiny-imagenet-200\\train\\"
TEST_DATA_PATH = "F:\\DataSet\\tiny-imagenet-200\\test\\"
DATASET_NAME = "tiny_imagenet-200"
NUM_CLASSES = 200  # 6 :small oil  10 cifar 10
IMG_HEIGHT = 250 # 250  # The images are already resized here
IMG_WIDTH = 250 # 250  # The images are already resized here

SEED = 42
TRAIN_RATIO = 0.75
VAL_RATIO = 1 - TRAIN_RATIO
SHUFFLE_BUFFER_SIZE = 100

LEARNING_RATE = 1e-3
EPOCHS = 50
TRAIN_BATCH_SIZE = 32  # Let's see, I don't have GPU, Google Colab is best hope
TEST_BATCH_SIZE = 32  # Let's see, I don't have GPU, Google Colab is best hope
FULL_BATCH_SIZE = 32

ENC_LOSS_WEIGHT = 0.05
DEC_LOSS_WEIGHT = 0.05
CLS_LOSS_WEIGHT = 0.9

######################################
# resizer模块相关参数
in_channels= 1       # Number of input channels of resizer (for RGB images it is 3)
out_channels= 1      # Number of output channels of resizer (for RGB images it is 3)
num_kernels = 16      # Same as `n` in paper
num_resblocks = 2     # Same as 'r' in paper
negative_slope = 0.2  # Used by leaky relu
interpolate_mode= "bilinear"  # Passed to torch.nn.functional.interpolate
image_size = 250 # 250
resizer_image_size =  250 # 224
###### Train and Test time #########

DATA_PATH = "../data/small_data_oil_for_classification/images/all/"
AUTOENCODER_MODEL_PATH = "baseline_autoencoder.pt"
ENCODER_MODEL_PATH = "../models/resnet_capsule_models/{}/resnet18_capsule_encoder_epoch{}_multitask.pt".format(DATASET_NAME,str(LEARNING_RATE),EPOCHS)
DECODER_MODEL_PATH = "../models/resnet_capsule_models/{}/resnet18_capsule_decoder_epoch{}_multitask.pt".format(DATASET_NAME,str(LEARNING_RATE),EPOCHS)
EMBEDDING_PATH = "../models/resnet_capsule_models/{}/data_embedding_f.npy"
EMBEDDING_SHAPE = (1, NUM_CLASSES, 128)
MODEL_DIR_PATH = "../models/resnet_capsule_models/"
# TEST_RATIO = 0.2

###### Test time #########
NUM_IMAGES = 10
# TEST_IMAGE_PATH = "../data/small_data_oil_for_classification/test/liefeng/0014.png"
TEST_IMAGE_PATH = "F:\\DataSet\\tiny-imagenet-200\\test\\n01443537\\n01443537_0.JPEG"
